Tao Zhou^1,3, Yunfeng Pan^1,3,*, Huiling Lu², Pei Dang^1,3, Yujie Guo^1,3, Yaxing Wang^1,3

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4813-4832, 2024, DOI:10.32604/cmc.2024.054685 - 12 September 2024

Abstract Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion. Such as Positron Emission Computed Tomography (PET), Computed Tomography (CT), and PET-CT. How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions. To solve the problem, the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network (Guide-YNet) is proposed in this paper. Firstly, a double-encoder single-decoder U-Net is used as the backbone in this model, a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and… More >

Qiurong Qin, Yueqin Li^*, Yajie Mi, Jinhui Shen, Kexin Wu, Zhenzhao Wang

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 843-874, 2024, DOI:10.32604/cmc.2024.051769 - 18 July 2024

Abstract In the era of the Internet, widely used web applications have become the target of hacker attacks because they contain a large amount of personal information. Among these vulnerabilities, stealing private data through cross-site scripting (XSS) attacks is one of the most commonly used attacks by hackers. Currently, deep learning-based XSS attack detection methods have good application prospects; however, they suffer from problems such as being prone to overfitting, a high false alarm rate, and low accuracy. To address these issues, we propose a multi-stage feature extraction and fusion model for XSS detection based on… More >

Xuejie Wang¹, Jianxun Zhang^1,*, Ye Tao², Xiaoli Yuan¹, Yifan Guo¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4621-4639, 2024, DOI:10.32604/cmc.2024.051556 - 20 June 2024

Abstract While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity… More >

Tao Zhou^1,3, Yujie Guo^1,3,*, Caiyue Peng^1,3, Yuxia Niu^1,3, Yunfeng Pan^1,3, Huiling Lu²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4863-4882, 2024, DOI:10.32604/cmc.2024.050767 - 20 June 2024

Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the… More >

Ning Li^*, Shucai Huang, Daozhi Wei

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3511-3531, 2023, DOI:10.32604/cmc.2023.045987 - 26 December 2023

Abstract This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet (ISTD-CenterNet) network for detecting small infrared targets in complex environments. The method eliminates the need for an anchor frame, addressing the issues of low accuracy and slow speed. HRNet is used as the framework for feature extraction, and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects. A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire… More >

Chengfei Hua^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 893-909, 2023, DOI:10.32604/cmc.2023.040862 - 31 October 2023

Abstract With the development of social media and the prevalence of mobile devices, an increasing number of people tend to use social media platforms to express their opinions and attitudes, leading to many online controversies. These online controversies can severely threaten social stability, making automatic detection of controversies particularly necessary. Most controversy detection methods currently focus on mining features from text semantics and propagation structures. However, these methods have two drawbacks: 1) limited ability to capture structural features and failure to learn deeper structural features, and 2) neglecting the influence of topic information and ineffective utilization… More >

Dehai Zhang^*, Yu Ma, Qing Liu, Haoxing Wang, Anquan Ren, Jiashu Liang

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2901-2920, 2023, DOI:10.32604/cmc.2023.038264 - 08 October 2023

Abstract Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images, ensuring road safety while providing an important decision-making function for sustainable transportation. In order to provide a comprehensive and reasonable description of complex traffic scenes, a traffic scene semantic captioning model with multi-stage feature enhancement is proposed in this paper. In general, the model follows an encoder-decoder structure. First, multi-level granularity visual features are used for feature enhancement during the encoding process, which enables the model to learn… More >